Super-resolution apparatus and method for virtual and mixed reality

ABSTRACT

An apparatus and method for efficiently improving virtual/real interactions in augmented reality. For example, one embodiment of a method comprises: capturing a raw image including depth data; identifying one or more regions of interest based on a detected spatial proximity of one or more virtual objects and one or more real objects; generating a super-resolution map of the one or more regions of interest using machine-learning techniques or results thereof; detecting interactions between the virtual objects and the real objects using the super-resolution map; and performing one or more graphics processing or general purpose processing operations based on the detected interactions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 15/719,458,filed Sep. 28, 2017, which is hereby incorporated by reference.

TECHNICAL FIELD

This invention relates generally to the field of graphics processors.More particularly, the invention relates to a super-resolution apparatusand method for virtual and augmented reality.

BACKGROUND ART

Virtual reality (VR) refers to data processing technologies thatreplicate a real or imagined environment by simulating a user's physicalpresence in that environment and, in some implementations, the user isprovided with the ability to interact with the environment. Many currentVR environments are displayed either on a computer screen or with aspecial virtual reality headset. Some simulations include additionalsensory information such as sound through speakers or headphonestargeted towards VR users.

In contrast to VR, which fully replaces the real world with a virtualone, mixed realty (MR) provides for a view of the physical, real-worldenvironment whose elements have been augmented by supplemental sensoryinput such as graphics, audio, video, and/or GPS data. Mixed realitysystems merge real and virtual worlds to produce novel visualizationswhere physical and digital objects co-exist and interact in real time.

Current dense reconstruction of virtual and mixed environments islimited by the resolution of depth cameras and computation power. Forexample, spatial mapping of the Hololens has an extremely sparse voxelrepresentation, and the experience of playing mixed reality games islimited. It is hard for a fine virtual object to precisely interact withthe real world given the large voxel size.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the followingdrawings, in which:

FIG. 1 is a block diagram of an embodiment of a computer system with aprocessor having one or more processor cores and graphics processors;

FIG. 2 illustrates a processor on which embodiments of the invention maybe implemented;

FIG. 3 illustrates an exemplary graphics processor on which embodimentsof the invention may be implemented;

FIG. 4 illustrates an exemplary graphics processing engine on whichembodiments of the invention may be implemented;

FIG. 5 illustrates an exemplary architecture on which embodiments of theinvention may be implemented;

FIGS. 6A-B illustrate exemplary scheduling and execution circuitry onwhich embodiments of the invention may be implemented;

FIG. 7 illustrates exemplary graphics processor instruction format whichmay be used by embodiments of the invention;

FIG. 8 illustrates an exemplary graphics processer including a commandstreamer, thread dispatcher and execution logic on which embodiments ofthe invention may be implemented;

FIGS. 9A-B illustrate exemplary graphics command formats and commandsequences which may be utilized by embodiments of the invention;

FIG. 10 illustrates an exemplary data processing system on whichembodiments of the invention may be implemented;

FIG. 11A illustrates an exemplary IP core development components usablein accordance with certain embodiments of the invention;

FIG. 11B illustrates an exemplary semiconductor package in accordancewith embodiments of the invention;

FIG. 12 illustrates an exemplary system on a chip (SoC) on whichembodiments of the invention may be implemented;

FIGS. 13A-B illustrate exemplary graphics processor architectures onwhich embodiments of the invention may be implemented;

FIGS. 14A-B illustrate additional details of exemplary graphicsprocessor architectures on which embodiments of the invention may beimplemented;

FIGS. 15A-C illustrate different embodiments of the invention havingmultiple graphics engines/pipelines;

FIG. 16 illustrates one embodiment which performs foviation control overone or more pipeline stages;

FIG. 17 illustrates time warping performed in accordance with oneembodiment of the invention;

FIG. 18 illustrates audio processing in accordance with one embodimentof the invention;

FIG. 19 illustrates a physics engine employed in one embodiment of theinvention;

FIG. 20 illustrates one embodiment which includes lens-matched shadingand multi-projection circuitry;

FIG. 21 illustrates one embodiment of a distributed virtual realityimplementation;

FIG. 22 illustrates one embodiment of a method for a distributed virtualreality implementation;

FIG. 23 illustrates one embodiment of the invention in which superresolution techniques are applied to regions of interest;

FIG. 24 illustrates machine learning techniques employed to generate atrained model usable to process regions of interest; and

FIG. 25 illustrates a method in accordance with one embodiment of theinvention.

DETAILED DESCRIPTION System Overview

FIG. 1 is a block diagram of a processing system 100, according to anembodiment. In various embodiments the system 100 includes one or moreprocessors 102 and one or more graphics processors 108, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 102 or processorcores 107. In one embodiment, the system 100 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

In one embodiment the system 100 can include, or be incorporated withina server-based gaming platform, a game console, including a game andmedia console, a mobile gaming console, a handheld game console, or anonline game console. In some embodiments the system 100 is a mobilephone, smart phone, tablet computing device or mobile Internet device.The processing system 100 can also include, couple with, or beintegrated within a wearable device, such as a smart watch wearabledevice, smart eyewear device, augmented reality device, or virtualreality device. In some embodiments, the processing system 100 is atelevision or set top box device having one or more processors 102 and agraphical interface generated by one or more graphics processors 108.

In some embodiments, the one or more processors 102 each include one ormore processor cores 107 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 107 is configured to process aspecific instruction set 109. In some embodiments, instruction set 109may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 107 may each process adifferent instruction set 109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 107may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 102 includes cache memory 104.Depending on the architecture, the processor 102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 102. In some embodiments, the processor 102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 107 using knowncache coherency techniques. A register file 106 is additionally includedin processor 102 which may include different types of registers forstoring different types of data (e.g., integer registers, floating pointregisters, status registers, and an instruction pointer register). Someregisters may be general-purpose registers, while other registers may bespecific to the design of the processor 102.

In some embodiments, one or more processor(s) 102 are coupled with oneor more interface bus(es) 110 to transmit communication signals such asaddress, data, or control signals between processor 102 and othercomponents in the system 100. The interface bus 110, in one embodiment,can be a processor bus, such as a version of the Direct Media Interface(DMI) bus. However, processor busses are not limited to the DMI bus, andmay include one or more Peripheral Component Interconnect buses (e.g.,PCI, PCI Express), memory busses, or other types of interface busses. Inone embodiment the processor(s) 102 include an integrated memorycontroller 116 and a platform controller hub 130. The memory controller116 facilitates communication between a memory device and othercomponents of the system 100, while the platform controller hub (PCH)130 provides connections to I/O devices via a local I/O bus.

The memory device 120 can be a dynamic random access memory (DRAM)device, a static random access memory (SRAM) device, flash memorydevice, phase-change memory device, or some other memory device havingsuitable performance to serve as process memory. In one embodiment thememory device 120 can operate as system memory for the system 100, tostore data 122 and instructions 121 for use when the one or moreprocessors 102 executes an application or process. Memory controller 116also couples with an optional external graphics processor 112, which maycommunicate with the one or more graphics processors 108 in processors102 to perform graphics and media operations. In some embodiments adisplay device 111 can connect to the processor(s) 102. The displaydevice 111 can be one or more of an internal display device, as in amobile electronic device or a laptop device or an external displaydevice attached via a display interface (e.g., DisplayPort, etc.). Inone embodiment the display device 111 can be a head mounted display(HMD) such as a stereoscopic display device for use in virtual reality(VR) applications or augmented reality (AR) applications.

In some embodiments the platform controller hub 130 enables peripheralsto connect to memory device 120 and processor 102 via a high-speed I/Obus. The I/O peripherals include, but are not limited to, an audiocontroller 146, a network controller 134, a firmware interface 128, awireless transceiver 126, touch sensors 125, a data storage device 124(e.g., hard disk drive, flash memory, etc.). The data storage device 124can connect via a storage interface (e.g., SATA) or via a peripheralbus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). The touch sensors 125 can include touch screen sensors,pressure sensors, or fingerprint sensors. The wireless transceiver 126can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile networktransceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver.The firmware interface 128 enables communication with system firmware,and can be, for example, a unified extensible firmware interface (UEFI).The network controller 134 can enable a network connection to a wirednetwork. In some embodiments, a high-performance network controller (notshown) couples with the interface bus 110. The audio controller 146, inone embodiment, is a multi-channel high definition audio controller. Inone embodiment the system 100 includes an optional legacy I/O controller140 for coupling legacy (e.g., Personal System 2 (PS/2)) devices to thesystem. The platform controller hub 130 can also connect to one or moreUniversal Serial Bus (USB) controllers 142 connect input devices, suchas keyboard and mouse 143 combinations, a camera 144, or other USB inputdevices.

It will be appreciated that the system 100 shown is exemplary and notlimiting, as other types of data processing systems that are differentlyconfigured may also be used. For example, an instance of the memorycontroller 116 and platform controller hub 130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 112. In one embodiment the platform controller hub 130 and/ormemory controller 160 may be external to the one or more processor(s)102. For example, the system 100 can include an external memorycontroller 116 and platform controller hub 130, which may be configuredas a memory controller hub and peripheral controller hub within a systemchipset that is in communication with the processor(s) 102.

FIG. 2 is a block diagram of an embodiment of a processor 200 having oneor more processor cores 202A-202N, an integrated memory controller 214,and an integrated graphics processor 208. Those elements of FIG. 2having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor200 can include additional cores up to and including additional core202N represented by the dashed lined boxes. Each of processor cores202A-202N includes one or more internal cache units 204A-204N. In someembodiments each processor core also has access to one or more sharedcached units 206.

The internal cache units 204A-204N and shared cache units 206 representa cache memory hierarchy within the processor 200. The cache memoryhierarchy may include at least one level of instruction and data cachewithin each processor core and one or more levels of shared mid-levelcache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or otherlevels of cache, where the highest level of cache before external memoryis classified as the LLC. In some embodiments, cache coherency logicmaintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or morebus controller units 216 and a system agent core 210. The one or morebus controller units 216 manage a set of peripheral buses, such as oneor more PCI or PCI express busses. System agent core 210 providesmanagement functionality for the various processor components. In someembodiments, system agent core 210 includes one or more integratedmemory controllers 214 to manage access to various external memorydevices (not shown).

In some embodiments, one or more of the processor cores 202A-202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 202A-202N during multi-threaded processing. System agentcore 210 may additionally include a power control unit (PCU), whichincludes logic and components to regulate the power state of processorcores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphicsprocessor 208 to execute graphics processing operations. In someembodiments, the graphics processor 208 couples with the set of sharedcache units 206, and the system agent core 210, including the one ormore integrated memory controllers 214. In some embodiments, the systemagent core 210 also includes a display controller 211 to drive graphicsprocessor output to one or more coupled displays. In some embodiments,display controller 211 may also be a separate module coupled with thegraphics processor via at least one interconnect, or may be integratedwithin the graphics processor 208.

In some embodiments, a ring based interconnect unit 212 is used tocouple the internal components of the processor 200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 212 via an I/O link213.

The exemplary I/O link 213 represents at least one of multiple varietiesof I/O interconnects, including an on package I/O interconnect whichfacilitates communication between various processor components and ahigh-performance embedded memory module 218, such as an eDRAM module. Insome embodiments, each of the processor cores 202A-202N and graphicsprocessor 208 use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 202A-202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 202A-202N are heterogeneous in terms of instruction setarchitecture (ISA), where one or more of processor cores 202A-202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 202A-202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor200 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 3 is a block diagram of a graphics processor 300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 300 includes amemory interface 314 to access memory. Memory interface 314 can be aninterface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 300 also includes a displaycontroller 302 to drive display output data to a display device 320.Display controller 302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. The display device 320 can be an internal orexternal display device. In one embodiment the display device 320 is ahead mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In some embodiments,graphics processor 300 includes a video codec engine 306 to encode,decode, or transcode media to, from, or between one or more mediaencoding formats, including, but not limited to Moving Picture ExpertsGroup (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formatssuch as H.264/MPEG-4 AVC, as well as the Society of Motion Picture &Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic ExpertsGroup (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block imagetransfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, GPE 310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 312 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 306. In some embodiments, media pipeline 316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executingthreads spawned by 3D pipeline 312 and media pipeline 316. In oneembodiment, the pipelines send thread execution requests to 3D/Mediasubsystem 315, which includes thread dispatch logic for arbitrating anddispatching the various requests to available thread executionresources. The execution resources include an array of graphicsexecution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 315 includes one or more internal cachesfor thread instructions and data. In some embodiments, the subsystemalso includes shared memory, including registers and addressable memory,to share data between threads and to store output data.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 410 is a version of theGPE 310 shown in FIG. 3. Elements of FIG. 4 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein, but are not limited to such. For example, the 3D pipeline 312and media pipeline 316 of FIG. 3 are illustrated. The media pipeline 316is optional in some embodiments of the GPE 410 and may not be explicitlyincluded within the GPE 410. For example and in at least one embodiment,a separate media and/or image processor is coupled to the GPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer403, which provides a command stream to the 3D pipeline 312 and/or mediapipelines 316. In some embodiments, command streamer 403 is coupled withmemory, which can be system memory, or one or more of internal cachememory and shared cache memory. In some embodiments, command streamer403 receives commands from the memory and sends the commands to 3Dpipeline 312 and/or media pipeline 316. The commands are directivesfetched from a ring buffer, which stores commands for the 3D pipeline312 and media pipeline 316. In one embodiment, the ring buffer canadditionally include batch command buffers storing batches of multiplecommands. The commands for the 3D pipeline 312 can also includereferences to data stored in memory, such as but not limited to vertexand geometry data for the 3D pipeline 312 and/or image data and memoryobjects for the media pipeline 316. The 3D pipeline 312 and mediapipeline 316 process the commands and data by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to a graphics core array 414. In one embodiment thegraphics core array 414 include one or more blocks of graphics cores(e.g., graphics core(s) 415A, graphics core(s) 415B), each blockincluding one or more graphics cores. Each graphics core includes a setof graphics execution resources that includes general-purpose andgraphics specific execution logic to perform graphics and computeoperations, as well as fixed function texture processing and/or machinelearning and artificial intelligence acceleration logic.

In various embodiments the 3D pipeline 312 includes fixed function andprogrammable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing theinstructions and dispatching execution threads to the graphics corearray 414. The graphics core array 414 provides a unified block ofexecution resources for use in processing these shader programs.Multi-purpose execution logic (e.g., execution units) within thegraphics core(s) 415A-414B of the graphic core array 414 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments the graphics core array 414 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallelgeneral-purpose computational operations, in addition to graphicsprocessing operations. The general-purpose logic can perform processingoperations in parallel or in conjunction with general-purpose logicwithin the processor core(s) 107 of FIG. 1 or core 202A-202N as in FIG.2.

Output data generated by threads executing on the graphics core array414 can output data to memory in a unified return buffer (URB) 418. TheURB 418 can store data for multiple threads. In some embodiments the URB418 may be used to send data between different threads executing on thegraphics core array 414. In some embodiments the URB 418 mayadditionally be used for synchronization between threads on the graphicscore array and fixed function logic within the shared function logic420.

In some embodiments, graphics core array 414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 410. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 414 couples with shared function logic 420 thatincludes multiple resources that are shared between the graphics coresin the graphics core array. The shared functions within the sharedfunction logic 420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 414. In variousembodiments, shared function logic 420 includes but is not limited tosampler 421, math 422, and inter-thread communication (ITC) 423 logic.Additionally, some embodiments implement one or more cache(s) 425 withinthe shared function logic 420.

A shared function is implemented where the demand for a givenspecialized function is insufficient for inclusion within the graphicscore array 414. Instead a single instantiation of that specializedfunction is implemented as a stand-alone entity in the shared functionlogic 420 and shared among the execution resources within the graphicscore array 414. The precise set of functions that are shared between thegraphics core array 414 and included within the graphics core array 414varies across embodiments. In some embodiments, specific sharedfunctions within the shared function logic 420 that are used extensivelyby the graphics core array 414 may be included within shared functionlogic 416 within the graphics core array 414. In various embodiments,the shared function logic 416 within the graphics core array 414 caninclude some or all logic within the shared function logic 420. In oneembodiment, all logic elements within the shared function logic 420 maybe duplicated within the shared function logic 416 of the graphics corearray 414. In one embodiment the shared function logic 420 is excludedin favor of the shared function logic 416 within the graphics core array414.

FIG. 5 is a block diagram of hardware logic of a graphics processor core500, according to some embodiments described herein. Elements of FIG. 5having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Theillustrated graphics processor core 500, in some embodiments, isincluded within the graphics core array 414 of FIG. 4. The graphicsprocessor core 500, sometimes referred to as a core slice, can be one ormultiple graphics cores within a modular graphics processor. Thegraphics processor core 500 is exemplary of one graphics core slice, anda graphics processor as described herein may include multiple graphicscore slices based on target power and performance envelopes. Eachgraphics core 500 can include a fixed function block 530 coupled withmultiple sub-cores 501A-501F, also referred to as sub-slices, thatinclude modular blocks of general-purpose and fixed function logic.

In some embodiments the fixed function block 530 includes ageometry/fixed function pipeline 536 that can be shared by all sub-coresin the graphics processor 500, for example, in lower performance and/orlower power graphics processor implementations. In various embodiments,the geometry/fixed function pipeline 536 includes a 3D fixed functionpipeline (e.g., 3D pipeline 312 as in FIG. 3 and FIG. 4) a videofront-end unit, a thread spawner and thread dispatcher, and a unifiedreturn buffer manager, which manages unified return buffers, such as theunified return buffer 418 of FIG. 4.

In one embodiment the fixed function block 530 also includes a graphicsSoC interface 537, a graphics microcontroller 538, and a media pipeline539. The graphics SoC interface 537 provides an interface between thegraphics core 500 and other processor cores within a system on a chipintegrated circuit. The graphics microcontroller 538 is a programmablesub-processor that is configurable to manage various functions of thegraphics processor 500, including thread dispatch, scheduling, andpre-emption. The media pipeline 539 (e.g., media pipeline 316 of FIG. 3and FIG. 4) includes logic to facilitate the decoding, encoding,pre-processing, and/or post-processing of multimedia data, includingimage and video data. The media pipeline 539 implement media operationsvia requests to compute or sampling logic within the sub-cores 501-501F.

In one embodiment the SoC interface 537 enables the graphics core 500 tocommunicate with general-purpose application processor cores (e.g.,CPUs) and/or other components within an SoC, including memory hierarchyelements such as a shared last level cache memory, the system RAM,and/or embedded on-chip or on-package DRAM. The SoC interface 537 canalso enable communication with fixed function devices within the SoC,such as camera imaging pipelines, and enables the use of and/orimplements global memory atomics that may be shared between the graphicscore 500 and CPUs within the SoC. The SoC interface 537 can alsoimplement power management controls for the graphics core 500 and enablean interface between a clock domain of the graphic core 500 and otherclock domains within the SoC. In one embodiment the SoC interface 537enables receipt of command buffers from a command streamer and globalthread dispatcher that are configured to provide commands andinstructions to each of one or more graphics cores within a graphicsprocessor. The commands and instructions can be dispatched to the mediapipeline 539, when media operations are to be performed, or a geometryand fixed function pipeline (e.g., geometry and fixed function pipeline536, geometry and fixed function pipeline 514) when graphics processingoperations are to be performed.

The graphics microcontroller 538 can be configured to perform variousscheduling and management tasks for the graphics core 500. In oneembodiment the graphics microcontroller 538 can perform graphics and/orcompute workload scheduling on the various graphics parallel engineswithin execution unit (EU) arrays 502A-502F, 504A-504F within thesub-cores 501A-501F. In this scheduling model, host software executingon a CPU core of an SoC including the graphics core 500 can submitworkloads one of multiple graphic processor doorbells, which invokes ascheduling operation on the appropriate graphics engine. Schedulingoperations include determining which workload to run next, submitting aworkload to a command streamer, pre-empting existing workloads runningon an engine, monitoring progress of a workload, and notifying hostsoftware when a workload is complete. In one embodiment the graphicsmicrocontroller 538 can also facilitate low-power or idle states for thegraphics core 500, providing the graphics core 500 with the ability tosave and restore registers within the graphics core 500 across low-powerstate transitions independently from the operating system and/orgraphics driver software on the system.

The graphics core 500 may have greater than or fewer than theillustrated sub-cores 501A-501F, up to N modular sub-cores. For each setof N sub-cores, the graphics core 500 can also include shared functionlogic 510, shared and/or cache memory 512, a geometry/fixed functionpipeline 514, as well as additional fixed function logic 516 toaccelerate various graphics and compute processing operations. Theshared function logic 510 can include logic units associated with theshared function logic 420 of FIG. 4 (e.g., sampler, math, and/orinter-thread communication logic) that can be shared by each N sub-coreswithin the graphics core 500. The shared and/or cache memory 512 can bea last-level cache for the set of N sub-cores 501A-501F within thegraphics core 500, and can also serve as shared memory that isaccessible by multiple sub-cores. The geometry/fixed function pipeline514 can be included instead of the geometry/fixed function pipeline 536within the fixed function block 530 and can include the same or similarlogic units.

In one embodiment the graphics core 500 includes additional fixedfunction logic 516 that can include various fixed function accelerationlogic for use by the graphics core 500. In one embodiment the additionalfixed function logic 516 includes an additional geometry pipeline foruse in position only shading. In position-only shading, two geometrypipelines exist, the full geometry pipeline within the geometry/fixedfunction pipeline 516, 536, and a cull pipeline, which is an additionalgeometry pipeline which may be included within the additional fixedfunction logic 516. In one embodiment the cull pipeline is a trimmeddown version of the full geometry pipeline. The full pipeline and thecull pipeline can execute different instances of the same application,each instance having a separate context. Position only shading can hidelong cull runs of discarded triangles, enabling shading to be completedearlier in some instances. For example and in one embodiment the cullpipeline logic within the additional fixed function logic 516 canexecute position shaders in parallel with the main application andgenerally generates critical results faster than the full pipeline, asthe cull pipeline fetches and shades only the position attribute of thevertices, without performing rasterization and rendering of the pixelsto the frame buffer. The cull pipeline can use the generated criticalresults to compute visibility information for all the triangles withoutregard to whether those triangles are culled. The full pipeline (whichin this instance may be referred to as a replay pipeline) can consumethe visibility information to skip the culled triangles to shade onlythe visible triangles that are finally passed to the rasterizationphase.

In one embodiment the additional fixed function logic 516 can alsoinclude machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

Within each graphics sub-core 501A-501F includes a set of executionresources that may be used to perform graphics, media, and computeoperations in response to requests by graphics pipeline, media pipeline,or shader programs. The graphics sub-cores 501A-501F include multiple EUarrays 502A-502F, 504A-504F, thread dispatch and inter-threadcommunication (TD/IC) logic 503A-503F, a 3D (e.g., texture) sampler505A-505F, a media sampler 506A-506F, a shader processor 507A-507F, andshared local memory (SLM) 508A-508F. The EU arrays 502A-502F, 504A-504Feach include multiple execution units, which are general-purposegraphics processing units capable of performing floating-point andinteger/fixed-point logic operations in service of a graphics, media, orcompute operation, including graphics, media, or compute shaderprograms. The TD/IC logic 503A-503F performs local thread dispatch andthread control operations for the execution units within a sub-core andfacilitate communication between threads executing on the executionunits of the sub-core. The 3D sampler 505A-505F can read texture orother 3D graphics related data into memory. The 3D sampler can readtexture data differently based on a configured sample state and thetexture format associated with a given texture. The media sampler506A-506F can perform similar read operations based on the type andformat associated with media data. In one embodiment, each graphicssub-core 501A-501F can alternately include a unified 3D and mediasampler. Threads executing on the execution units within each of thesub-cores 501A-501F can make use of shared local memory 508A-508F withineach sub-core, to enable threads executing within a thread group toexecute using a common pool of on-chip memory.

Execution Units

FIGS. 6A-6B illustrate thread execution logic 600 including an array ofprocessing elements employed in a graphics processor core according toembodiments described herein. Elements of FIGS. 6A-6B having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. FIG. 6A illustrates anoverview of thread execution logic 600, which can include a variant ofthe hardware logic illustrated with each sub-core 501A-501F of FIG. 5.FIG. 6B illustrates exemplary internal details of an execution unit.

As illustrated in FIG. 6A, in some embodiments thread execution logic600 includes a shader processor 602, a thread dispatcher 604,instruction cache 606, a scalable execution unit array including aplurality of execution units 608A-608N, a sampler 610, a data cache 612,and a data port 614. In one embodiment the scalable execution unit arraycan dynamically scale by enabling or disabling one or more executionunits (e.g., any of execution unit 608A, 608B, 608C, 608D, through608N−1 and 608N) based on the computational requirements of a workload.In one embodiment the included components are interconnected via aninterconnect fabric that links to each of the components. In someembodiments, thread execution logic 600 includes one or more connectionsto memory, such as system memory or cache memory, through one or more ofinstruction cache 606, data port 614, sampler 610, and execution units608A-608N. In some embodiments, each execution unit (e.g. 608A) is astand-alone programmable general-purpose computational unit that iscapable of executing multiple simultaneous hardware threads whileprocessing multiple data elements in parallel for each thread. Invarious embodiments, the array of execution units 608A-608N is scalableto include any number individual execution units.

In some embodiments, the execution units 608A-608N are primarily used toexecute shader programs. A shader processor 602 can process the variousshader programs and dispatch execution threads associated with theshader programs via a thread dispatcher 604. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units 608A-608N.For example, a geometry pipeline can dispatch vertex, tessellation, orgeometry shaders to the thread execution logic for processing. In someembodiments, thread dispatcher 604 can also process runtime threadspawning requests from the executing shader programs.

In some embodiments, the execution units 608A-608N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 608A-608N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units608A-608N causes a waiting thread to sleep until the requested data hasbeen returned. While the waiting thread is sleeping, hardware resourcesmay be devoted to processing other threads. For example, during a delayassociated with a vertex shader operation, an execution unit can performoperations for a pixel shader, fragment shader, or another type ofshader program, including a different vertex shader.

Each execution unit in execution units 608A-608N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 608A-608N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into afused execution unit 609A-609N having thread control logic (607A-607N)that is common to the fused EUs. Multiple EUs can be fused into an EUgroup. Each EU in the fused EU group can be configured to execute aseparate SIMD hardware thread. The number of EUs in a fused EU group canvary according to embodiments. Additionally, various SIMD widths can beperformed per-EU, including but not limited to SIMD8, SIMD16, andSIMD32. Each fused graphics execution unit 609A-609N includes at leasttwo execution units. For example, fused execution unit 609A includes afirst EU 608A, second EU 608B, and thread control logic 607A that iscommon to the first EU 608A and the second EU 608B. The thread controllogic 607A controls threads executed on the fused graphics executionunit 609A, allowing each EU within the fused execution units 609A-609Nto execute using a common instruction pointer register.

One or more internal instruction caches (e.g., 606) are included in thethread execution logic 600 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,612) are included to cache thread data during thread execution. In someembodiments, a sampler 610 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 610 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 600 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor602 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 602 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 602dispatches threads to an execution unit (e.g., 608A) via threaddispatcher 604. In some embodiments, shader processor 602 uses texturesampling logic in the sampler 610 to access texture data in texture mapsstored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 614 provides a memory accessmechanism for the thread execution logic 600 to output processed data tomemory for further processing on a graphics processor output pipeline.In some embodiments, the data port 614 includes or couples to one ormore cache memories (e.g., data cache 612) to cache data for memoryaccess via the data port.

As illustrated in FIG. 6B, a graphics execution unit 608 can include aninstruction fetch unit 637, a general register file array (GRF) 624, anarchitectural register file array (ARF) 626, a thread arbiter 622, asend unit 630, a branch unit 632, a set of SIMD floating point units(FPUs) 634, and in one embodiment a set of dedicated integer SIMD ALUs635. The GRF 624 and ARF 626 includes the set of general register filesand architecture register files associated with each simultaneoushardware thread that may be active in the graphics execution unit 608.In one embodiment, per thread architectural state is maintained in theARF 626, while data used during thread execution is stored in the GRF624. The execution state of each thread, including the instructionpointers for each thread, can be held in thread-specific registers inthe ARF 626.

In one embodiment the graphics execution unit 608 has an architecturethat is a combination of Simultaneous Multi-Threading (SMT) andfine-grained Interleaved Multi-Threading (IMT). The architecture has amodular configuration that can be fine tuned at design time based on atarget number of simultaneous threads and number of registers perexecution unit, where execution unit resources are divided across logicused to execute multiple simultaneous threads.

In one embodiment, the graphics execution unit 608 can co-issue multipleinstructions, which may each be different instructions. The threadarbiter 622 of the graphics execution unit thread 608 can dispatch theinstructions to one of the send unit 630, branch unit 642, or SIMDFPU(s) 634 for execution. Each execution thread can access 128general-purpose registers within the GRF 624, where each register canstore 32 bytes, accessible as a SIMD 8-element vector of 32-bit dataelements. In one embodiment, each execution unit thread has access to 4Kbytes within the GRF 624, although embodiments are not so limited, andgreater or fewer register resources may be provided in otherembodiments. In one embodiment up to seven threads can executesimultaneously, although the number of threads per execution unit canalso vary according to embodiments. In an embodiment in which seventhreads may access 4 Kbytes, the GRF 624 can store a total of 28 Kbytes.Flexible addressing modes can permit registers to be addressed togetherto build effectively wider registers or to represent strided rectangularblock data structures.

In one embodiment, memory operations, sampler operations, and otherlonger-latency system communications are dispatched via “send”instructions that are executed by the message passing send unit 630. Inone embodiment, branch instructions are dispatched to a dedicated branchunit 632 to facilitate SIMD divergence and eventual convergence.

In one embodiment the graphics execution unit 608 includes one or moreSIMD floating point units (FPU(s)) 634 to perform floating-pointoperations. In one embodiment, the FPU(s) 634 also support integercomputation. In one embodiment the FPU(s) 634 can SIMD execute up to Mnumber of 32-bit floating-point (or integer) operations, or SIMD executeup to 2M 16-bit integer or 16-bit floating-point operations. In oneembodiment, at least one of the FPU(s) provides extended math capabilityto support high-throughput transcendental math functions and doubleprecision 64-bit floating-point. In some embodiments, a set of 8-bitinteger SIMD ALUs 635 are also present, and may be specificallyoptimized to perform operations associated with machine learningcomputations.

In one embodiment, arrays of multiple instances of the graphicsexecution unit 608 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). For scalability, product architects can chose theexact number of execution units per sub-core grouping. In one embodimentthe execution unit 608 can execute instructions across a plurality ofexecution channels. In a further embodiment, each thread executed on thegraphics execution unit 608 is executed on a different channel.

FIG. 7 is a block diagram illustrating a graphics processor instructionformats 700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 700 described and illustrated are macro-instructions,in that they are instructions supplied to the execution unit, as opposedto micro-operations resulting from instruction decode once theinstruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 710. A 64-bitcompacted instruction format 730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 730. The native instructions availablein the 64-bit format 730 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 713. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format710.

For each format, instruction opcode 712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 710 an exec-size field716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 716 is not available foruse in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including twosource operands, src0 720, src1 722, and one destination 718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 724), where the instructionopcode 712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726, which specifies an address mode and/or anaccess mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes amix of instructions, including synchronization instructions (e.g., wait,send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instructiongroup 748 includes component-wise arithmetic instructions (e.g., add,multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel mathgroup 748 performs the arithmetic operations in parallel across datachannels. The vector math group 750 includes arithmetic instructions(e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math groupperforms arithmetic such as dot product calculations on vector operands.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 8 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 800 includes a geometry pipeline820, a media pipeline 830, a display engine 840, thread execution logic850, and a render output pipeline 870. In some embodiments, graphicsprocessor 800 is a graphics processor within a multi-core processingsystem that includes one or more general-purpose processing cores. Thegraphics processor is controlled by register writes to one or morecontrol registers (not shown) or via commands issued to graphicsprocessor 800 via a ring interconnect 802. In some embodiments, ringinterconnect 802 couples graphics processor 800 to other processingcomponents, such as other graphics processors or general-purposeprocessors. Commands from ring interconnect 802 are interpreted by acommand streamer 803, which supplies instructions to individualcomponents of the geometry pipeline 820 or the media pipeline 830.

In some embodiments, command streamer 803 directs the operation of avertex fetcher 805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 803. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 807 execute vertex-processing instructions by dispatchingexecution threads to execution units 852A-852B via a thread dispatcher831.

In some embodiments, execution units 852A-852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 852A-852B have anattached L1 cache 851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, geometry pipeline 820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 813 operatesat the direction of hull shader 811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to geometry pipeline 820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 811, tessellator 813, and domain shader 817) can bebypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 819 via one or more threads dispatched to executionunits 852A-852B, or can proceed directly to the clipper 829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader819 receives input from the vertex shader 807. In some embodiments,geometry shader 819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 processes vertex data. The clipper829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 873 in the render output pipeline870 dispatches pixel shaders to convert the geometric objects into perpixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 850. In some embodiments, anapplication can bypass the rasterizer and depth test component 873 andaccess un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric,or some other interconnect mechanism that allows data and messagepassing amongst the major components of the processor. In someembodiments, execution units 852A-852B and associated logic units (e.g.,L1 cache 851, sampler 854, texture cache 858, etc.) interconnect via adata port 856 to perform memory access and communicate with renderoutput pipeline components of the processor. In some embodiments,sampler 854, caches 851, 858 and execution units 852A-852B each haveseparate memory access paths. In one embodiment the texture cache 858can also be configured as a sampler cache.

In some embodiments, render output pipeline 870 contains a rasterizerand depth test component 873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache 878and depth cache 879 are also available in some embodiments. A pixeloperations component 877 performs pixel-based operations on the data,though in some instances, pixel operations associated with 2D operations(e.g. bit block image transfers with blending) are performed by the 2Dengine 841, or substituted at display time by the display controller 843using overlay display planes. In some embodiments, a shared L3 cache 875is available to all graphics components, allowing the sharing of datawithout the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes amedia engine 837 and a video front-end 834. In some embodiments, videofront-end 834 receives pipeline commands from the command streamer 803.In some embodiments, media pipeline 830 includes a separate commandstreamer. In some embodiments, video front-end 834 processes mediacommands before sending the command to the media engine 837. In someembodiments, media engine 837 includes thread spawning functionality tospawn threads for dispatch to thread execution logic 850 via threaddispatcher 831.

In some embodiments, graphics processor 800 includes a display engine840. In some embodiments, display engine 840 is external to processor800 and couples with the graphics processor via the ring interconnect802, or some other interconnect bus or fabric. In some embodiments,display engine 840 includes a 2D engine 841 and a display controller843. In some embodiments, display engine 840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, the geometry pipeline 820 and media pipeline 830are configurable to perform operations based on multiple graphics andmedia programming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor commandformat 900 according to some embodiments. FIG. 9B is a block diagramillustrating a graphics processor command sequence 910 according to anembodiment. The solid lined boxes in FIG. 9A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 900 of FIG. 9A includes data fields to identify a client902, a command operation code (opcode) 904, and data 906 for the commandA sub-opcode 905 and a command size 908 are also included in somecommands.

In some embodiments, client 902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands Oncethe command is received by the client unit, the client unit reads theopcode 904 and, if present, sub-opcode 905 to determine the operation toperform. The client unit performs the command using information in datafield 906. For some commands an explicit command size 908 is expected tospecify the size of the command. In some embodiments, the command parserautomatically determines the size of at least some of the commands basedon the command opcode. In some embodiments commands are aligned viamultiples of a double word.

The flow diagram in FIG. 9B illustrates an exemplary graphics processorcommand sequence 910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 922 and the media pipeline 924 do notoperate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands In response toa pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 913 isrequired only once within an execution context before issuing pipelinecommands unless the context is to issue commands for both pipelines. Insome embodiments, a pipeline flush command 912 is required immediatelybefore a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures agraphics pipeline for operation and is used to program the 3D pipeline922 and the media pipeline 924. In some embodiments, pipeline controlcommand 914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 916 includes selecting the size and number of returnbuffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 920,the command sequence is tailored to the 3D pipeline 922 beginning withthe 3D pipeline state 930 or the media pipeline 924 beginning at themedia pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 922 dispatches shader execution threads to graphicsprocessor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 910 followsthe media pipeline 924 path when performing media operations. Ingeneral, the specific use and manner of programming for the mediapipeline 924 depends on the media or compute operations to be performed.Specific media decode operations may be offloaded to the media pipelineduring media decode. In some embodiments, the media pipeline can also bebypassed and media decode can be performed in whole or in part usingresources provided by one or more general-purpose processing cores. Inone embodiment, the media pipeline also includes elements forgeneral-purpose graphics processor unit (GPGPU) operations, where thegraphics processor is used to perform SIMD vector operations usingcomputational shader programs that are not explicitly related to therendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similarmanner as the 3D pipeline 922. A set of commands to configure the mediapipeline state 940 are dispatched or placed into a command queue beforethe media object commands 942. In some embodiments, commands for themedia pipeline state 940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 940 also support the use of one ormore pointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 942. Once the pipeline state is configured andmedia object commands 942 are queued, the media pipeline 924 istriggered via an execute command 944 or an equivalent execute event(e.g., register write). Output from media pipeline 924 may then be postprocessed by operations provided by the 3D pipeline 922 or the mediapipeline 924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system 1000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1010, an operating system 1020, and at least one processor 1030. In someembodiments, processor 1030 includes a graphics processor 1032 and oneor more general-purpose processor core(s) 1034. The graphics application1010 and operating system 1020 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 1010 contains one or moreshader programs including shader instructions 1012. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 1014 in a machinelanguage suitable for execution by the general-purpose processor core1034. The application also includes graphics objects 1016 defined byvertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1020 can support agraphics API 1022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 1020uses a front-end shader compiler 1024 to compile any shader instructions1012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1010. In some embodiments, the shader instructions 1012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-endshader compiler 1027 to convert the shader instructions 1012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1012 in the GLSL high-level language are passed to a usermode graphics driver 1026 for compilation. In some embodiments, usermode graphics driver 1026 uses operating system kernel mode functions1028 to communicate with a kernel mode graphics driver 1029. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 11A is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility1130 can generate a software simulation 1110 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation1110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 1112. The simulation model 1112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 1115 can then be created or synthesized from thesimulation model 1112. The RTL design 1115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 1115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by thedesign facility into a hardware model 1120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3rdparty fabrication facility 1165 using non-volatile memory 1140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 1150 or wireless connection 1160. Thefabrication facility 1165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 11B illustrates a cross-section side view of an integrated circuitpackage assembly 1170, according to some embodiments described herein.The integrated circuit package assembly 1170 illustrates animplementation of one or more processor or accelerator devices asdescribed herein. The package assembly 1170 includes multiple units ofhardware logic 1172, 1174 connected to a substrate 1180. The logic 1172,1174 may be implemented at least partly in configurable logic orfixed-functionality logic hardware, and can include one or more portionsof any of the processor core(s), graphics processor(s), or otheraccelerator devices described herein. Each unit of logic 1172, 1174 canbe implemented within a semiconductor die and coupled with the substrate1180 via an interconnect structure 1173. The interconnect structure 1173may be configured to route electrical signals between the logic 1172,1174 and the substrate 1180, and can include interconnects such as, butnot limited to bumps or pillars. In some embodiments, the interconnectstructure 1173 may be configured to route electrical signals such as,for example, input/output (I/O) signals and/or power or ground signalsassociated with the operation of the logic 1172, 1174. In someembodiments, the substrate 1180 is an epoxy-based laminate substrate.The package substrate 1180 may include other suitable types ofsubstrates in other embodiments. The package assembly 1170 can beconnected to other electrical devices via a package interconnect 1183.The package interconnect 1183 may be coupled to a surface of thesubstrate 1180 to route electrical signals to other electrical devices,such as a motherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 1172, 1174 are electricallycoupled with a bridge 1182 that is configured to route electricalsignals between the logic 1172, 1174. The bridge 1182 may be a denseinterconnect structure that provides a route for electrical signals. Thebridge 1182 may include a bridge substrate composed of glass or asuitable semiconductor material. Electrical routing features can beformed on the bridge substrate to provide a chip-to-chip connectionbetween the logic 1172, 1174.

Although two units of logic 1172, 1174 and a bridge 1182 areillustrated, embodiments described herein may include more or fewerlogic units on one or more dies. The one or more dies may be connectedby zero or more bridges, as the bridge 1182 may be excluded when thelogic is included on a single die. Alternatively, multiple dies or unitsof logic can be connected by one or more bridges. Additionally, multiplelogic units, dies, and bridges can be connected together in otherpossible configurations, including three-dimensional configurations.

Exemplary System on a Chip Integrated Circuit

FIGS. 12-14 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 1200includes one or more application processor(s) 1205 (e.g., CPUs), atleast one graphics processor 1210, and may additionally include an imageprocessor 1215 and/or a video processor 1220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 1200 includes peripheral or bus logic including a USBcontroller 1225, UART controller 1230, an SPI/SDIO controller 1235, andan I2S/I2C controller 1240. Additionally, the integrated circuit caninclude a display device 1245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 1255. Storage maybe provided by a flash memory subsystem 1260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine1270.

FIGS. 13A-13B are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 13A illustrates an exemplary graphics processor 1310 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to an embodiment. FIG. 13B illustrates anadditional exemplary graphics processor 1340 of a system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment. Graphics processor 1310 of FIG. 13A is anexample of a low power graphics processor core. Graphics processor 1340of FIG. 13B is an example of a higher performance graphics processorcore. Each of the graphics processors 1310, 1340 can be variants of thegraphics processor 1210 of FIG. 12.

As shown in FIG. 13A, graphics processor 1310 includes a vertexprocessor 1305 and one or more fragment processor(s) 1315A-1315N (e.g.,1315A, 1315B, 1315C, 1315D, through 1315N−1, and 1315N). Graphicsprocessor 1310 can execute different shader programs via separate logic,such that the vertex processor 1305 is optimized to execute operationsfor vertex shader programs, while the one or more fragment processor(s)1315A-1315N execute fragment (e.g., pixel) shading operations forfragment or pixel shader programs. The vertex processor 1305 performsthe vertex processing stage of the 3D graphics pipeline and generatesprimitives and vertex data. The fragment processor(s) 1315A-1315N usethe primitive and vertex data generated by the vertex processor 1305 toproduce a framebuffer that is displayed on a display device. In oneembodiment, the fragment processor(s) 1315A-1315N are optimized toexecute fragment shader programs as provided for in the OpenGL API,which may be used to perform similar operations as a pixel shaderprogram as provided for in the Direct 3D API.

Graphics processor 1310 additionally includes one or more memorymanagement units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuitinterconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B providefor virtual to physical address mapping for the graphics processor 1310,including for the vertex processor 1305 and/or fragment processor(s)1315A-1315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 1325A-1325B. In one embodiment the one or more MMU(s)1320A-1320B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 1205, image processor 1215, and/or video processor 1220 ofFIG. 12, such that each processor 1205-1220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 1330A-1330B enable graphics processor 1310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

As shown FIG. 13B, graphics processor 1340 includes the one or moreMMU(s) 1320A-1320B, caches 1325A-1325B, and circuit interconnects1330A-1330B of the graphics processor 1310 of FIG. 13A. Graphicsprocessor 1340 includes one or more shader core(s) 1355A-1355N (e.g.,1455A, 1355B, 1355C, 1355D, 1355E, 1355F, through 1355N−1, and 1355N),which provides for a unified shader core architecture in which a singlecore or type or core can execute all types of programmable shader code,including shader program code to implement vertex shaders, fragmentshaders, and/or compute shaders. The exact number of shader corespresent can vary among embodiments and implementations. Additionally,graphics processor 1340 includes an inter-core task manager 1345, whichacts as a thread dispatcher to dispatch execution threads to one or moreshader cores 1355A-1355N and a tiling unit 1358 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space, for example to exploit localspatial coherence within a scene or to optimize use of internal caches.

FIGS. 14A-14B illustrate additional exemplary graphics processor logicaccording to embodiments described herein. FIG. 14A illustrates agraphics core 1400 that may be included within the graphics processor1210 of FIG. 12, and may be a unified shader core 1355A-1355N as in FIG.13B. FIG. 14B illustrates a highly-parallel general-purpose graphicsprocessing unit 1430 suitable for deployment on a multi-chip module.

As shown in FIG. 14A, the graphics core 1400 includes a sharedinstruction cache 1402, a texture unit 1418, and a cache/shared memory1420 that are common to the execution resources within the graphics core1400. The graphics core 1400 can include multiple slices 1401A-1401N orpartition for each core, and a graphics processor can include multipleinstances of the graphics core 1400. The slices 1401A-1401N can includesupport logic including a local instruction cache 1404A-1404N, a threadscheduler 1406A-1406N, a thread dispatcher 1408A-1408N, and a set ofregisters 1410A. To perform logic operations, the slices 1401A-1401N caninclude a set of additional function units (AFUs 1412A-1412N),floating-point units (FPU 1414A-1414N), integer arithmetic logic units(ALUs 1416-1416N), address computational units (ACU 1413A-1413N),double-precision floating-point units (DPFPU 1415A-1415N), and matrixprocessing units (MPU 1417A-1417N).

Some of the computational units operate at a specific precision. Forexample, the FPUs 1414A-1414N can perform single-precision (32-bit) andhalf-precision (16-bit) floating point operations, while the DPFPUs1415A-1415N perform double precision (64-bit) floating point operations.The ALUs 1416A-1416N can perform variable precision integer operationsat 8-bit, 16-bit, and 32-bit precision, and can be configured for mixedprecision operations. The MPUs 1417A-1417N can also be configured formixed precision matrix operations, including half-precision floatingpoint and 8-bit integer operations. The MPUs 1417-1417N can perform avariety of matrix operations to accelerate machine learning applicationframeworks, including enabling support for accelerated general matrix tomatrix multiplication (GEMM). The AFUs 1412A-1412N can performadditional logic operations not supported by the floating-point orinteger units, including trigonometric operations (e.g., Sine, Cosine,etc.).

As shown in FIG. 14B, a general-purpose processing unit (GPGPU) 1430 canbe configured to enable highly-parallel compute operations to beperformed by an array of graphics processing units. Additionally, theGPGPU 1430 can be linked directly to other instances of the GPGPU tocreate a multi-GPU cluster to improve training speed for particularlydeep neural networks. The GPGPU 1430 includes a host interface 1432 toenable a connection with a host processor. In one embodiment the hostinterface 1432 is a PCI Express interface. However, the host interfacecan also be a vendor specific communications interface or communicationsfabric. The GPGPU 1430 receives commands from the host processor anduses a global scheduler 1434 to distribute execution threads associatedwith those commands to a set of compute clusters 1436A-1436H. Thecompute clusters 1436A-1436H share a cache memory 1438. The cache memory1438 can serve as a higher-level cache for cache memories within thecompute clusters 1436A-1436H.

The GPGPU 1430 includes memory 1434A-1434B coupled with the computeclusters 1436A-1436H via a set of memory controllers 1442A-1442B. Invarious embodiments, the memory 1434A-1434B can include various types ofmemory devices including dynamic random access memory (DRAM) or graphicsrandom access memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory.

In one embodiment the compute clusters 1436A-1436H each include a set ofgraphics cores, such as the graphics core 1400 of FIG. 14A, which caninclude multiple types of integer and floating point logic units thatcan perform computational operations at a range of precisions includingsuited for machine learning computations. For example and in oneembodiment at least a subset of the floating point units in each of thecompute clusters 1436A-1436H can be configured to perform 16-bit or32-bit floating point operations, while a different subset of thefloating point units can be configured to perform 64-bit floating pointoperations.

Multiple instances of the GPGPU 1430 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment the multiple instances of the GPGPU 1430 communicate over thehost interface 1432. In one embodiment the GPGPU 1430 includes an I/Ohub 1439 that couples the GPGPU 1430 with a GPU link 1440 that enables adirect connection to other instances of the GPGPU. In one embodiment theGPU link 1440 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 1430. In one embodiment the GPU link 1440 couples with a highspeed interconnect to transmit and receive data to other GPGPUs orparallel processors. In one embodiment the multiple instances of theGPGPU 1430 are located in separate data processing systems andcommunicate via a network device that is accessible via the hostinterface 1432. In one embodiment the GPU link 1440 can be configured toenable a connection to a host processor in addition to or as analternative to the host interface 1432.

While the illustrated configuration of the GPGPU 1430 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 1430 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration the GPGPU 1430 includes fewer of the computeclusters 1436A-1436H relative to the training configuration.Additionally, the memory technology associated with the memory1434A-1434B may differ between inferencing and training configurations,with higher bandwidth memory technologies devoted to trainingconfigurations. In one embodiment the inferencing configuration of theGPGPU 1430 can support inferencing specific instructions. For example,an inferencing configuration can provide support for one or more 8-bitinteger dot product instructions, which are commonly used duringinferencing operations for deployed neural networks.

Exemplary Virtual/Mixed Reality Architectures

A. Overview

Embodiments of the invention may be implemented within a virtual realitysystem such as that illustrated in FIG. 15A which includes a graphicssystem component 1580 and a head-mounted display (HMD) 1550. In oneembodiment, the HMD 1550 comprises a right display 1551 on which imagesframes are rendered for viewing by the user's right eye and a leftdisplay 1552 on which image frames are rendered for viewing by theuser's left eye. Separate graphics engines, 1556 and 1557, includegraphics processing pipelines for rendering the right and left imageframes, respectively, in response to the execution of a particularvirtual reality application 1561. Each of the graphics engines 1556-1557may comprise a separate graphics processing unit (GPU). Alternatively,the graphics engines 1556-1557 may include different sets of graphicsexecution resources within a single GPU or spread across multiple GPUs.For example, in a virtualized environment, a separate virtual GPU (vGPU)may be allocated to each display 1551-1552. Regardless of how the GPUresources are partitioned, the graphics engines 1556-1557 may implementany of the graphics processing techniques described herein.

In one embodiment, a user/eye tracking device 1553 integrated on the HMD1550 includes sensors to detect the current orientation of the user'shead and the direction of the user's gaze. For example, the orientationof the user's head may be captured using optical sensors andaccelerometers while the current direction of the user's gaze may becaptured with optical eye tracking devices such as cameras. Asillustrated, the user/eye tracking device 1553 provide the user'scurrent view 1560 to the graphics system 1580, which then adjustgraphics processing accordingly (i.e., to ensure that the current imageframes being rendered are from the current perspective of the user).

In one embodiment, the virtual reality application 1561 utilizes agraphics application programming interface (API) 1562 to implementfeatures of the graphics engines 1556-1557 as described herein. Forexample, the graphics API 1562 may be provided with a virtual realitysoftware development kit (SDK) 1563 which a developer may use togenerate application program code for the virtual reality application1561. For example, the virtual reality SDK 1563 may include a compiler(and/or other design tools) to generate object code for the virtualreality application 1561 which uses the API 1562 (e.g., by making callsto functions/commands included in the API). One or more of thetechniques described herein may be implemented using the graphics API1562, hardware within the graphics engines 1556-1557, and/or acombination thereof.

FIG. 15B illustrates the various stages of the two graphics pipelinesfor the right and left displays 1551-1552 in accordance with oneembodiment. In particular, rasterization-based pipelines are illustratedincluding an input assembler (IA) 1521A-B which reads index and vertexdata and a vertex shader (VS) 1522A-B from memory 1515. As mentioned,commands may be received by the IA 1521A-B via the graphics API 1562.The vertex shader 1522A-B performs shading operations on each vertex(e.g., transforming each vertex's 3D position in virtual space to the 2Dcoordinate at which it appears on the screen) and generates results inthe form of primitives (e.g., triangles). A geometry shader (GS) 1523A-Btakes a whole primitive as input, possibly with adjacency information.For example, when operating on triangles, the three vertices are thegeometry shader's input. The geometry shader 1523A-B can then emit zeroor more primitives, which are rasterized at a rasterization stage1524A-B and the resulting fragments ultimately passed to a pixel shader(PS) 1525A-B, which performs shading operations on each of theindividual pixels which are stored, frame by frame, within a framebuffer 1526A-B prior to being displayed on the HMD.

In one embodiment, a global illumination graphics processingarchitecture such as a ray tracing architecture may be employed. FIG.15C, for example, illustrates an exemplary ray tracing-based graphicspipeline 1500 in which one or more pipeline stages 1501A-B to 1505A-Bperform ray-tracing based rendering for the left and right displays1551-1552. The illustrated stages include a ray generation module1501A-B which generates rays for processing. For example, one embodimentperforms breadth-first ray tracing per image tile, where a tile denotesa small fixed-size rectangular region. In one embodiment of abreadth-first implementation, one ray per pixel is generated for eachiteration on the image tile. A ray traversal module 1502A-B traverseseach ray against a bounding volume hierarchy (BVH) or other accelerationdata structure. One or more intersection modules 1503A-B test the rayagainst one or more triangles or other primitives, and in the end, thetraversal and intersection units must find the closest primitive thateach ray intersects. One or more shader units 1504A-B then performshading operations on the resulting pixels which are stored, frame byframe, within a frame buffer 1505A-B prior to being displayed on the HMD1550.

B. Foveated Rendering

One embodiment of the invention employs foveated rendering, a digitalimage processing technique in which the image resolution, or amount ofdetail, varies across the image in accordance with one or more “fixationpoints.” A fixation point indicates the highest resolution region of theimage and corresponds to the fovea, the center of the eye's retina. Thelocation of a fixation point may be specified in different ways. Forexample, eye tracking devices which precisely measure the eye's positionand movement are used to determine fixation points in virtual realityimplementations. A higher resolution may be used in a region surroundingthe fixation point than in other regions of the image. For example, asillustrated in FIG. 16, a foveation control module 1620 may control therasterizer 1404 to use a higher sample or pixel density for the foveatedarea of the image.

C. Time Warping

Some embodiments of the invention may be employed in a VR system whichuses time warping. Time warping is a technique used to improveperformance in current virtual reality (VR) systems. According to thistechnique, each image frame is rendered in accordance with the currentorientation of the user's head and/or eyes (i.e., as read from an eyetracking device and/or other sensors on the head mounted display (HMD)to detect the motion of the user's head). Just before displaying thenext image frame, the sensor data is captured again and is used totransform the scene to fit the most recent sensor data (i.e., “warping”the current image frame). By taking advantage of the depth maps (i.e., ZBuffers) which have already been generated, time warping can moveobjects in 3D space with relatively low computational requirements.

One embodiment will be described with respect to FIG. 17 whichillustrates a graphics processing engine 1300 communicatively coupled toa head-mounted display (HMD) 1350. A VR application 1310 is executed,generating graphics data and commands to be executed by the graphicsprocessing engine 1300. The graphics processing engine 1300 may includeone or more graphics processing units (GPUs) including a graphicspipeline to execute the graphics commands and render the image frames tobe displayed on the HMD 1350 (e.g., such as the graphics pipelinesdescribed herein). For simplicity, only a single display 1717 is shownin FIG. 17, which may be the left and/or right display.

In operation, an image rendering module 1305 renders image frames to bedisplayed in the left and right displays 1717. In one embodiment, eachimage is rendered in accordance with a current orientation of the user'shead and/or eyes, as provided by user/eye tracking module 1353integrated on the HMD 1350. In particular, the HMD 1350 may includevarious sensors to track the current orientation of the user's head andcameras and associated circuitry/logic to track the current focus of theuser's eyes. In a virtual reality implementation, this data is used torender left/right images from the correct perspective (i.e., based onthe direction and focus of the user's current gaze).

While illustrated as a single component in FIG. 17 for simplicity,separate image rendering circuitry and logic may be used for the leftand right image frames. Moreover, various other graphics pipeline stagesare not illustrated to avoid obscuring the underlying principles of theinvention including, for example, a vertex shader, geometry shader, andtexture mapper. A ray tracing architecture employed in one embodimentmay include a ray generation module, a ray traversal module, anintersection module, and a shading module. In any implementation, therendering module 1705 renders images for the left and right displays1717 based on the current orientation/gaze of the user.

In the illustrated embodiment, a first frame buffer 1716 is storing animage frame N−1, currently displayed within the left/right display 1717of the HMD. The next image frame to be displayed (image frame N) is thenrendered within a second frame buffer 1715. In one embodiment, the imagerendering module 1705 uses the coordinate data provided by the user/eyetracking module 1553 to render the next frame within frame buffer 1715.At the time the next frame needs to be displayed within the left and/orright display 1717, time warp module 1720 transforms image frame N−1 orimage frame N (if rendering of image frame N is complete) to fit themost recent sensor data provided by user/eye tracking module 1553. Thistransformation is performed by the time warp module 1720 using thepreviously-generated depth maps stored in the processing engine'sZ-buffers 1718. The transformation moves objects in 3D space withrelatively small computational requirements, resulting in a morerecently completed product without the need to re-render the scene.Thus, in most cases, it should be substantially similar to the imageframe which would have been rendered if rendering had occurred morequickly.

D. Additional VR Embodiments

As illustrated in FIG. 18, in one embodiment, audio processing logic1802 produces left and right audio streams in response to the currentview 1860. In particular, in one embodiment, the audio processing logic1802 generates audio for a left speaker 1851 and a right speaker 1852integrated on the HMD 1550 in accordance with the current orientation ofthe user's head within the virtual environment. For example, if a carpasses to the left of the user, then the audio processing logic 1802will cause the sound of the car to be more pronounced in the leftspeaker 1851 to produce a more realistic effect. The audio processinglogic 1802 may implement various types of audio processing techniquesincluding, by way of example and not limitation, Dolby Digital Cinema,Dolby 3D, DTS Headphone:X, and DTS Neo:PC, to name a few.

As illustrated in FIG. 19, one embodiment of the invention includes aphysics engine 1901 to provide realistic modelling for touchinteractions and haptic feedback. This may be accomplished throughadditional user tracking devices 1953 which may include, for example,touch interactivity using hand controllers, positional tracking, andhaptics. One embodiment of the physics engine 1901 detects when a handcontroller interacts with a virtual object and enables the graphicsengines 1301-1302 and/or VR application 1310 to provide aphysically-accurate visual and haptic response. The physics engine 1901may also model the physical behavior of the virtual world to ensure thatall interactions are accurate and behave as would be expected in thereal world.

As illustrated in FIG. 20, embodiments of the invention may employmulti-resolution shading and/or lens-matched shading 2001 within thepixel shading stage of the graphics engine(s) 1501-1502. In oneembodiment, multi-resolution shading is a rendering technique forvirtual reality in which each part of an image is rendered at aresolution that better matches the pixel density of the lens correctedimage. Dedicated GPU circuitry may be used which is capable of renderingmultiple scaled viewports in a single pass. In one embodiment,lens-matched shading utilizes multi-projection hardware within the GPUto significantly improve pixel shading performance In particular, thisembodiment renders to a surface that more closely approximates the lenscorrected image that is output to the left/right displays 1551-1552.This embodiment avoids rendering many pixels that would otherwise bediscarded before the image is output to the HMD 1550.

In one embodiment, multi-projection circuitry 2002 includes asimultaneous multi-projection architecture which renders geometry onlyonce and then simultaneously projects both right-eye and left-eye viewsof the geometry within the left/right displays 1551-1552. Thisarchitecture significantly reduces processing resources required intraditional virtual reality applications which draw geometry twice(i.e., once for the left eye, and once for the right eye). As a result,the geometric complexity of virtual reality applications is effectivelydoubled.

E. Server-Based VR Embodiments

One embodiment of the invention comprises a distributed virtual reality(VR) architecture in which a high power server or “compute cluster” iscoupled to a VR render node over a network. In one embodiment, thecompute cluster performs all of the graphics processing using, forexample, a ray tracing graphics pipeline which generates image frames,compresses the image frames, and then transmits the compressed imageframes to the render node for decompression and display. In oneembodiment, the compute cluster executes a graphics application andgenerates samples using global illumination techniques such as raytracing. It then streams the samples to a render node over a network. Inone embodiment, the compute cluster determines the samples to begenerated/streamed based on an expected viewpoint provided by the rendernode, which has a GPU for performing light field rendering and iscoupled to a VR display such as a head mounted display (HMD). Thecompute cluster continually generates the stream of samples which arestored within a buffer on the render node. The GPU of the render nodeconsumes the samples from the buffer to render the light field for theVR display.

FIG. 21 illustrates an exemplary compute cluster 2100 communicativelycoupled to a render node 2160 over a network 2120. In one embodiment,the compute cluster 2100 includes high-performance graphics processingresources for executing global illumination/ray tracing operations togenerate samples (e.g. GPUs, CPUs, memory, execution units, etc) whichare then used by the render node 2160 to perform light field renderingon a virtual reality apparatus 2150 (such as a HMD). In particular, inthe illustrated embodiment, the compute cluster 2100 includes globalillumination/ray tracing circuitry and/or logic 2105 (hereinafter “GImodule 2105”) for performing global illumination/ray tracing operationsin response to a virtual reality application 2104. A stream of samplesare generated by the GI module 2105 which may then be filtered and/orcompressed by filtering/compression module 2110. The filtered/compressedsamples are then streamed via a network interface to the render node2160 over a network 2120, which may be any form of data communicationnetwork (e.g., a public network such as the Internet or a private localarea network or wide area network, or a combination of different networktypes).

If the samples were compressed prior to transmission, then they aredecompressed by a decompression module 2130 on the render node 2160before being stored in a sample buffer 2131. A GPU 2165 on the rendernode 2160 consumes the samples from the sample buffer 2131 to render thelight field for image frames displayed on the VR display 2150. Inparticular, in one embodiment, sample insertion logic 2135asynchronously inserts samples into the light field which is rendered bylight field rendering logic 2140.

As illustrated in FIG. 21, in one embodiment, viewpoint analysis andprocessing logic 2145 receives an indication of the user's currentviewpoint from the VR apparatus 2150 and (potentially in combinationwith prior stored viewpoint data), determines an “expected” viewpoint,which it provides to both the GPU 2165 on the render node 2160 and tothe compute cluster 2100. As used herein, the “viewpoint” refers to theorientation of the user's gaze within the virtual reality environment(e.g., the direction in which the user is looking and/or focusing). TheVR apparatus 2150 may use a variety of sensors to determine the user'sviewpoint including, for example, eye tracking sensors to determine thelocation within each image frame at which the user's eyes are focusedand motion sensors such as accelerometers to determine the orientationof the user's head/body. Various other/additional sensors may be used todetermine the current viewpoint while still complying with theunderlying principles of the invention.

The viewpoint analysis and processing logic 2145 determines the expectedviewpoint based on a combination of the current viewpoint and priorviewpoints (e.g., determined from prior frames). For example, if theuser's viewpoint has been moving in a rightward direction over the pastN frames, then the expected viewpoint may be to the right of the currentviewpoint (i.e., since change of viewpoint may be expected to continuein the same direction). The amount of change may be calculated based onthe speed at which the viewpoint has been changing for the past frames.As such, the GI module 2105 may generate samples based on theexpectation that the user's viewpoint may continue moving in the samedirection. Thus, it will generate samples to cover portions of the imageframe from this viewpoint as well as viewpoints surrounding thisviewpoint (i.e., to ensure that the samples are available if theviewpoint does not continue linearly in the same direction). In asimilar manner, the GPU 2165 may retrieve samples from the sample buffer2131 based on the expected viewpoint, i.e., reading samples to cover theexpected viewpoint along with samples surrounding the expectedviewpoint.

In one embodiment, the compute cluster 2100 is implemented as acloud-based virtualized graphics processing service with an array ofgraphics resources dynamically allocated to clients, such as render node2160, upon demand. While only a single render node 2160 is illustratedin FIG. 21, many other render nodes may be concurrently connected to thecompute cluster 2100, which may allocate graphics processing resourcesas needed to support each individual VR implementation. In oneembodiment, the compute cluster supports a virtualized graphicsprocessing environment in which a virtual machine is allocated to eachrequesting client. Graphics processing resources may then be allocatedto the virtual machine based on the processing requirements of theclient. For example, for high performance applications (such as VR), oneor more full GPUs may be allocated to a client while for lowerperformance applications, a fraction of a GPU may be allocated to aclient. It should be noted, however, that the underlying principles ofthe invention are not limited to any particular compute clusterarchitecture.

A method in accordance with one embodiment is illustrated in FIG. 22.The method may be implemented within the context of the systemarchitectures described above but is not limited to any particularsystem architecture.

At 2200, the compute cluster receives an indication of the expectedviewpoint from the render node and, at 2201, generates samples based onthe expected viewpoint. As mentioned, the samples may be generated in aspecified region around the expected viewpoint to account for unexpectedmotion of the users head/eyes. At 2202 the compute cluster streams thesamples to a sample buffer in the render node over a network (e.g., theInternet). At 2203, the render node reads samples from the sample buffer(e.g., based on the viewpoint or the expected viewpoint) and inserts thesamples into the light field. At 2204, the render node GPU renders thelight field using the samples. At 2205, the render node receives thecurrent viewpoint, calculates the expected viewpoint, and transmits theexpected viewpoint to the compute cluster.

The embodiments of the invention described herein may be used toimplement a real time global illumination architecture such as a raytracing architecture for virtual reality. Because the bulk of thecomputations are performed on the compute cluster 2200, the render node2260 does not require the significant processing resources which wouldotherwise be required to perform ray tracing/global illumination.Rather, using these techniques, the GPU 2265 of the render node onlyrequires sufficient power to perform light field rendering usingpre-calculated samples stored in the sample buffer 2231.

Super-Resolution Apparatus and Method for Virtual and Mixed Reality

Environment understanding is critical in Mixed Reality (MR) systems forinterfacing virtual content with real world content. A betterunderstanding of real world surroundings results in smoother and moreseamless virtual-real object interaction, especially for fine control.Depth cameras on devices are typically used to capture depth map/colorimages of the scene and a 3D map is built from multiple camera views. Togenerate a high quality 3D model of the environment, high definition(HD) depth cameras are typically needed. These cameras can be veryexpensive and bulky, thus increasing cost and weight. In addition, dense3D model reconstruction is very computationally expensive, which canmake real-time implementation challenging.

In order to improve the user experience for virtual-real objectinteractions in MR systems without using expensive HD depth cameras anddense 3D reconstruction, the embodiments of the invention detect aRegion Of Interest (ROI) where the virtual and real objects interact andapply a depth super-resolution process within this ROI to generatehigher resolution depth data. One way to implement depthsuper-resolution is through implementation of a machine learningalgorithm such as a generative adversarial network (GAN) with depthfield statistics, although other implementations are possible. Theembodiments of the invention described herein can also be extended toimprove hand tracking, head tracking, etc., which are based on accuratedepth images.

In one embodiment, the ROI for real and virtual objects is determined bycomparing the distance of a virtual object to the user and the distanceof a real object to the user. Distances within a specified threshold mayindicate that the real and virtual objects can interact. These distancesmay be reported, for example, by the application engine (e.g., 3D gameengine) and/or depth camera. One embodiment of a deep learning-baseddepth super-resolution algorithm is applied on top of the ROI, toupscale the resolution by a specified amount (e.g., x2, x4, etc) thusimproving the detection and interaction accuracy.

One embodiment of the depth super-resolution algorithm leverages theadvantage of the GAN model and incorporates the characteristic of adepth map as a loss function for the model. Once generated, thesuper-resolution depth map can be propagated across frames to avoid theneed to compute the map at every instance. While certain specificdetails of a super-resolution process are set forth below, any depthsuper-resolution process may be used.

Super-resolution is a classic computer vision problem which is generallyused to upscale low resolution images to high resolution images. FIG. 23illustrates one embodiment of a framework to improve the interactionquality of virtual objects and real objects in a mixed realityenvironment. In the illustrated embodiment, components 2301-2305represent a typical process for 3D model generation. A depth camera 2301captures a depth map of the environment which is used by point cloudgenerator 2302 to formulate a corresponding point cloud. Featuredetection circuitry/logic 2303 extracts features in each point cloud at2303 which are used to match the adjacent point clouds. Point cloudregistration circuitry/logic 2304 registers all point clouds to generatethe final 3D point cloud model for the scene at 2305.

In one embodiment of the invention, a depth super-resolution module 2310comprising hardware (e.g., circuitry) and/or software (e.g., programcode executed on a CPU) performs supplemental processing on datacaptured from the depth camera 2301 to implement the super-resolutiontechniques described herein. In particular, region detectioncircuitry/logic 2306 determines the region(s) where each virtual objectinteracts with one or more real object(s). As mentioned above, theregion where a virtual object interacts with the environment may bedetermined by comparing the distances of the virtual and real objects tothe user. If no region is detected, the point cloud generator 2302 maygenerate the point cloud directly from the depth camera 2301. If theregion is detected, depth super-resolution circuitry/logic 2307 thenupscales the resolution of ROIs identified by the region detectioncircuitry/logic 2306 (e.g., ×2, ×4, etc). The point cloud generator thengenerates a point cloud using the upscaled ROIs. In one embodiment, theinput to the region detection circuitry/logic 2306 is a low resolutiondepth map, and the output of the depth-super-resolution circuitry/module2307 is high resolution depth map (at least for some ROIs).

As mentioned, one embodiment of the invention uses amachine-learning-based super-resolution algorithm (e.g., such as GAN)for analyzing depth images. FIG. 24 illustrates one embodiment of atraining architecture in which a set of high resolution (HR) depthimages 2401 are initially down-sampled by down-sampling circuitry/logic2402 to lower resolution images (e.g., by ×2 or ×4). A generator 2403uses the lower resolution images to generate a super resolution (SR)depth image 2404 which is the same size as the input HR depth image2401.

The generated SR depth images 2404 and original HR images 2401 are bothfed into a discriminator 2406, which attempts to distinguish between thereal depth image 2401 and the generated depth image 2401. In particular,the discriminator 2406, which may be implemented in circuitry, software,or any combination thereof, provides an identification of the real depthimage 2407 and generated depth image 2408. A model generator 2412 usesthe training results of the discriminator 2406 to generate a trainedmodel 2414 which may be continually updated and used by thediscriminator 2406 for subsequent discrimination attempts. In oneembodiment, the system is trained until the discriminator 2406 can nolonger distinguish between the generated and real images. Alternatively,the system may be trained until the discriminator reaches an acceptablelevel of accuracy.

In operation, the trained model 2414 is used to sample the depth mapreturned by the sensor in a mixed reality system and generate asuper-resolution map of the region of interest (ROI). For example, thetrained model 2414 may be implemented within the region detectioncircuitry/logic 2306 and/or the depth super-resolution circuitry/logic2307 in FIG. 23. The ROI may be determined by the region detectioncircuitry/logic 2306 using a variety of techniques including, but notlimited to, silhouette extraction and relaxation, bounding boxes,propagation using a prior frame's information or any such methods.During normal operation, the super-resolution processing using thetrained model 2414 may be applied to every frame or, for efficiencypurposes, every Nth frame, every second, or for some other finiteinterval larger than a single frame time.

In one embodiment, the nature of the depth images are coded into theloss function of the generative adversarial network. Instead of a meansquare error (MSE) loss function, the following is used in oneembodiment to recover realistic textures and fine-grained details fromdepth images that have been heavily down sampled.Loss_(Gen)=−log(L(SR _(depth) ,HR _(depth))+P(HR _(depth)))Loss_(Dis) =MSE(SR _(depth) ,HR _(depth))+P(HR _(depth))P(HR_(depth)) is a new item used in one embodiment to represent thetotal variation of the depth image. It extracts the gradients along Xand Y directions.

A method in accordance with one embodiment is illustrated in FIG. 24.The method may be implemented on the system and processor architecturesdescribed above but is not limited to any particular system/processorarchitecture.

At 2501, a raw image is captured including depth data. At 2502, one ormore regions of interest are identified within the raw image based onthe proximity of virtual and real objects to the user. For example, asdiscussed above, if the distance of a virtual object to the user and thedistance of a real object to the user are close to one another within aspecified threshold, then these objects may define a region of interest(i.e., because the potential for interaction between the real andvirtual images).

Once one or more regions of interest have been defined, at 2503 atrained model generated by machine-learning techniques is used togenerate a super-resolution map of the one or more regions of interest.As mentioned, the trained model may be generated by comparing a seriesof high resolution images with generated super-resolution images andanalyzing the results (see FIG. 24 and associated text). Using theimproved resolution, at 2504, interactions between the virtual objectsand real objects are detected using the super-resolution map. Inresponse to detected interactions, various different processingfunctions may be implemented (e.g., generating new graphics related tothe interaction, performing general purpose data processing operations,etc).

In summary, once depth super-resolution is applied to the region wherevirtual and real objects interact, the interaction will be more naturaland seamless, thereby significantly improving the immersive experienceprovided by mixed reality.

In embodiments, the term “engine” or “module” or “logic” may refer to,be part of, or include an application specific integrated circuit(ASIC), an electronic circuit, a processor (shared, dedicated, orgroup), and/or memory (shared, dedicated, or group) that execute one ormore software or firmware programs, a combinational logic circuit,and/or other suitable components that provide the describedfunctionality. In embodiments, an engine or a module may be implementedin firmware, hardware, software, or any combination of firmware,hardware, and software.

Embodiments of the invention may include various steps, which have beendescribed above. The steps may be embodied in machine-executableinstructions which may be used to cause a general-purpose orspecial-purpose processor to perform the steps. Alternatively, thesesteps may be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components.

As described herein, instructions may refer to specific configurationsof hardware such as application specific integrated circuits (ASICs)configured to perform certain operations or having a predeterminedfunctionality or software instructions stored in memory embodied in anon-transitory computer readable medium. Thus, the techniques shown inthe figures can be implemented using code and data stored and executedon one or more electronic devices (e.g., an end station, a networkelement, etc.). Such electronic devices store and communicate(internally and/or with other electronic devices over a network) codeand data using computer machine-readable media, such as non-transitorycomputer machine-readable storage media (e.g., magnetic disks; opticaldisks; random access memory; read only memory; flash memory devices;phase-change memory) and transitory computer machine-readablecommunication media (e.g., electrical, optical, acoustical or other formof propagated signals—such as carrier waves, infrared signals, digitalsignals, etc.).

In addition, such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage device of a given electronic device typically storescode and/or data for execution on the set of one or more processors ofthat electronic device. Of course, one or more parts of an embodiment ofthe invention may be implemented using different combinations ofsoftware, firmware, and/or hardware. Throughout this detaileddescription, for the purposes of explanation, numerous specific detailswere set forth in order to provide a thorough understanding of thepresent invention. It will be apparent, however, to one skilled in theart that the invention may be practiced without some of these specificdetails. In certain instances, well known structures and functions werenot described in elaborate detail in order to avoid obscuring thesubject matter of the present invention. Accordingly, the scope andspirit of the invention should be judged in terms of the claims whichfollow.

What is claimed is:
 1. An apparatus comprising: a camera to capture araw image; and a processor to perform operations on one or more virtualobjects and one or more real objects that are captured in the raw imageand that are detected to interact, wherein the detection of interactionscomprises: identifying one or more regions of interest based on adetected spatial proximity of the one or more virtual objects and theone or more real objects, wherein the spatial proximity detectionincludes measuring a first distance between a virtual object and a userand a second distance between a real object and the user, anddetermining the difference between the first and second distances,generating a super-resolution map of the one or more regions of interestusing machine-learning techniques or results thereof, and detecting theinteractions between the one or more virtual objects and the one or morereal objects using the super-resolution map.
 2. The apparatus of claim1, wherein the machine-learning techniques are implemented with agenerative adversarial network (GAN) with depth field statistics.
 3. Theapparatus of claim 1, wherein the machine-learning techniques performcomparisons between a plurality of high-resolution images and acorresponding plurality of super-resolution images generated fromdown-sampled versions of the high-resolution images and evaluate resultsof the comparisons.
 4. The apparatus of claim 1, wherein at least one ofthe results of the machine-learning techniques comprise a trained modelusable to generate the super-resolution map.
 5. The apparatus of claim 4wherein the machine-learning techniques incorporate a characteristic ofa depth map as a loss function for the trained model.
 6. The apparatusof claim 1, wherein identifying the one or more regions of interestfurther comprises: when the first and second distances are within aspecified threshold value, using the virtual object and real object todefine a first region of interest.
 7. The apparatus of claim 1, whereingenerating the super-resolution map comprises upscaling the one or moreregions of interest to increase resolution by a specified amount.
 8. Theapparatus of claim 1, wherein the processor is a graphics processingunit.
 9. A method comprising: capturing a raw image; and performingoperations on one or more virtual objects and one or more real objectsthat are captured in the raw image and that are detected to interact,wherein the detection of interactions comprises: identifying one or moreregions of interest based on a detected spatial proximity of the one ormore virtual objects and the one or more real objects, wherein thespatial proximity detection includes measuring a first distance betweena virtual object and a user and a second distance between a real objectand the user, and determining the difference between the first andsecond distances, generating a super-resolution map of the one or moreregions of interest using machine-learning techniques or resultsthereof, and detecting the interactions between the one or more virtualobjects and the one or more real objects using the super-resolution map.10. The method of claim 9, wherein the machine-learning techniques areimplemented with a generative adversarial network (GAN) with depth fieldstatistics.
 11. The method of claim 9, wherein the machine-learningtechniques perform comparisons between a plurality of high-resolutionimages and a corresponding plurality of super-resolution imagesgenerated from down-sampled versions of the high-resolution images andevaluate results of the comparisons.
 12. The method of claim 9, whereinidentifying the one or more regions of interest further comprises: whenthe first and second distances are within a specified threshold value,using the virtual object and real object to define a first region ofinterest.
 13. The method of claim 9, wherein generating thesuper-resolution map comprises upscaling the one or more regions ofinterest to increase resolution by a specified amount.
 14. Anon-transitory machine-readable storage medium having program codestored therein which, when executed by a machine, causes the machine toperform the operations of: capturing a raw image; and performingoperations on one or more virtual objects and one or more real objectsthat are captured in the raw image and that are detected to interact,wherein the detection of interactions comprises: identifying one or moreregions of interest based on a detected spatial proximity of the one ormore virtual objects and the one or more real objects, wherein thespatial proximity detection includes measuring a first distance betweena virtual object and a user and a second distance between a real objectand the user, and determining the difference between the first andsecond distances, generating a super-resolution map of the one or moreregions of interest using machine-learning techniques or resultsthereof, and detecting the interactions between the one or more virtualobjects and the one or more real objects using the super-resolution map.15. The non-transitory machine-readable storage medium of claim 14,wherein the machine-learning techniques are implemented with agenerative adversarial network (GAN) with depth field statistics. 16.The non-transitory machine-readable storage medium of claim 14, whereinthe machine-learning techniques perform comparisons between a pluralityof high-resolution images and a corresponding plurality ofsuper-resolution images generated from down-sampled versions of thehigh-resolution images and evaluate results of the comparisons.
 17. Thenon-transitory machine-readable storage medium of claim 14, wherein atleast one of the results of the machine-learning techniques comprise atrained model usable to generate the super-resolution map.
 18. Thenon-transitory machine-readable storage medium of claim 14, whereinidentifying the one or more regions of interest further comprises: whenthe first and second distances are within a specified threshold value,using the virtual object and real object to define a first region ofinterest.
 19. The non-transitory machine-readable storage medium ofclaim 14, wherein generating the super-resolution map comprisesupscaling the one or more regions of interest to increase resolution bya specified amount.
 20. The non-transitory machine-readable storagemedium of claim 14, wherein the operations on the one or more virtualobjects and one or more real objects are by a graphics processing unit.